1. Enough Mathematical Appetizers!
Let us look at something more interesting:
Algorithms
CMSC Discrete Structures

2. CMSC 203 - Discrete Structures
Algorithms
What is an algorithm?
An algorithm is a finite set of precise instructions for performing a computation or for solving a problem.
This is a rather vague definition. You will get to know a more precise and mathematically useful definition when you attend CS420.
But this one is good enough for now…
CMSC Discrete Structures

3. CMSC 203 - Discrete Structures
Algorithms
Properties of algorithms:
Input from a specified set,
Output from a specified set (solution),
Definiteness of every step in the computation,
Correctness of output for every possible input,
Finiteness of the number of calculation steps,
Effectiveness of each calculation step and
Generality for a class of problems.
CMSC Discrete Structures

4. CMSC 203 - Discrete Structures
Algorithm Examples
We will use a pseudocode to specify algorithms, which slightly reminds us of Basic and Pascal.
Example: an algorithm that finds the maximum element in a finite sequence
procedure max(a1, a2, …, an: integers)
max := a1
for i := 2 to n
if max < ai then max := ai
{max is the largest element}
CMSC Discrete Structures

5. CMSC 203 - Discrete Structures
Algorithm Examples
Another example: a linear search algorithm, that is, an algorithm that linearly searches a sequence for a particular element.
procedure linear_search(x: integer; a1, a2, …, an: integers)
i := 1
while (i  n and x  ai)
i := i + 1
if i  n then location := i
else location := 0
{location is the subscript of the term that equals x, or is zero if x is not found}
CMSC Discrete Structures

6. CMSC 203 - Discrete Structures
Algorithm Examples
If the terms in a sequence are ordered, a binary search algorithm is more efficient than linear search.
The binary search algorithm iteratively restricts the relevant search interval until it closes in on the position of the element to be located.
CMSC Discrete Structures

7. Algorithm Examples binary search for the letter ‘j’ search interval
a c d f g h j l m o p r s u v x z
center element
CMSC Discrete Structures

8. Algorithm Examples binary search for the letter ‘j’ search interval
a c d f g h j l m o p r s u v x z
center element
CMSC Discrete Structures

9. Algorithm Examples binary search for the letter ‘j’ search interval
a c d f g h j l m o p r s u v x z
center element
CMSC Discrete Structures

10. Algorithm Examples binary search for the letter ‘j’ search interval
a c d f g h j l m o p r s u v x z
center element
CMSC Discrete Structures

11. Algorithm Examples found ! binary search for the letter ‘j’
search interval
a c d f g h j l m o p r s u v x z
center element
found !
CMSC Discrete Structures

12. CMSC 203 - Discrete Structures
Algorithm Examples
procedure binary_search(x: integer; a1, a2, …, an: integers)
i := 1 {i is left endpoint of search interval}
j := n {j is right endpoint of search interval}
while (i < j)
begin
m := (i + j)/2
if x > am then i := m + 1
else j := m
end
if x = ai then location := i
else location := 0
{location is the subscript of the term that equals x, or is zero if x is not found}
CMSC Discrete Structures

13. CMSC 203 - Discrete Structures
Complexity
In general, we are not so much interested in the time and space complexity for small inputs.
For example, while the difference in time complexity between linear and binary search is meaningless for a sequence with n = 10, it is gigantic for n = 230.
CMSC Discrete Structures

14. CMSC 203 - Discrete Structures
Complexity
For example, let us assume two algorithms A and B that solve the same class of problems.
The time complexity of A is 5,000n, the one for B is 1.1n for an input with n elements.
For n = 10, A requires 50,000 steps, but B only 3, so B seems to be superior to A.
For n = 1000, however, A requires 5,000,000 steps, while B requires 2.51041 steps.
CMSC Discrete Structures

15. CMSC 203 - Discrete Structures
Complexity
This means that algorithm B cannot be used for large inputs, while algorithm A is still feasible.
So what is important is the growth of the complexity functions.
The growth of time and space complexity with increasing input size n is a suitable measure for the comparison of algorithms.
CMSC Discrete Structures

16. CMSC 203 - Discrete Structures
Complexity
Comparison: time complexity of algorithms A and B
Input Size
Algorithm A
Algorithm B n
5,000n
1.1n 10
50,000 3
100
500,000
13,781
1,000
5,000,000
2.51041
1,000,000
5109
4.8
CMSC Discrete Structures

17. CMSC 203 - Discrete Structures
Complexity
This means that algorithm B cannot be used for large inputs, while running algorithm A is still feasible.
So what is important is the growth of the complexity functions.
The growth of time and space complexity with increasing input size n is a suitable measure for the comparison of algorithms.
CMSC Discrete Structures

18. The Growth of Functions
The growth of functions is usually described using the big-O notation.
Definition: Let f and g be functions from the integers or the real numbers to the real numbers.
We say that f(x) is O(g(x)) if there are constants C and k such that
|f(x)|  C|g(x)|
whenever x > k.
CMSC Discrete Structures

19. The Growth of Functions
When we analyze the growth of complexity functions, f(x) and g(x) are always positive.
Therefore, we can simplify the big-O requirement to
f(x)  Cg(x) whenever x > k.
If we want to show that f(x) is O(g(x)), we only need to find one pair (C, k) (which is never unique).
CMSC Discrete Structures

20. The Growth of Functions
The idea behind the big-O notation is to establish an upper boundary for the growth of a function f(x) for large x.
This boundary is specified by a function g(x) that is usually much simpler than f(x).
We accept the constant C in the requirement
f(x)  Cg(x) whenever x > k,
because C does not grow with x.
We are only interested in large x, so it is OK if f(x) > Cg(x) for x  k.
CMSC Discrete Structures

21. The Growth of Functions
Example:
Show that f(x) = x2 + 2x + 1 is O(x2).
For x > 1 we have:
x2 + 2x + 1  x2 + 2x2 + x2
 x2 + 2x + 1  4x2
Therefore, for C = 4 and k = 1:
f(x)  Cx2 whenever x > k.
 f(x) is O(x2).
CMSC Discrete Structures

22. The Growth of Functions
Question: If f(x) is O(x2), is it also O(x3)?
Yes. x3 grows faster than x2, so x3 grows also faster than f(x).
Therefore, we always have to find the smallest simple function g(x) for which f(x) is O(g(x)).
CMSC Discrete Structures

23. The Growth of Functions
“Popular” functions g(n) are
n log n, 1, 2n, n2, n!, n, n3, log n
Listed from slowest to fastest growth: 1
log n n
n log n n2 n3 2n n!
CMSC Discrete Structures

24. The Growth of Functions
A problem that can be solved with polynomial worst-case complexity is called tractable.
Problems of higher complexity are called intractable.
Problems that no algorithm can solve are called unsolvable.
You will find out more about this in CS420.
CMSC Discrete Structures

25. CMSC 203 - Discrete Structures
Useful Rules for Big-O
For any polynomial f(x) = anxn + an-1xn-1 + … + a0, where a0, a1, …, an are real numbers,
f(x) is O(xn).
If f1(x) is O(g1(x)) and f2(x) is O(g2(x)), then (f1 + f2)(x) is O(max(g1(x), g2(x)))
If f1(x) is O(g(x)) and f2(x) is O(g(x)), then (f1 + f2)(x) is O(g(x)).
If f1(x) is O(g1(x)) and f2(x) is O(g2(x)), then (f1f2)(x) is O(g1(x) g2(x)).
CMSC Discrete Structures

26. CMSC 203 - Discrete Structures
Complexity Examples
What does the following algorithm compute?
procedure who_knows(a1, a2, …, an: integers)
m := 0
for i := 1 to n-1
for j := i + 1 to n
if |ai – aj| > m then m := |ai – aj|
{m is the maximum difference between any two numbers in the input sequence}
Comparisons: n-1 + n-2 + n-3 + … + 1
= (n – 1)n/2 = 0.5n2 – 0.5n
Time complexity is O(n2).
CMSC Discrete Structures

27. CMSC 203 - Discrete Structures
Complexity Examples
Another algorithm solving the same problem:
procedure max_diff(a1, a2, …, an: integers)
min := a1
max := a1
for i := 2 to n
if ai < min then min := ai
else if ai > max then max := ai
m := max - min
Comparisons: 2n - 2
Time complexity is O(n).
CMSC Discrete Structures